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Virtual Watershed

Virtual Watershed. NSF Biocomplexity in the Environment Program 2004-2008 Christopher Lant ( Geograph y) PI Steven Kraft, Jeff Beaulieu ( Agr. Economics ) John Nicklow ( Civil Engineering ) Michelle Zhu ( Computer Science ) Raja Sengupta ( Geography ) McGill

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Virtual Watershed

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  1. Virtual Watershed NSF Biocomplexity in the Environment Program 2004-2008 Christopher Lant (Geography) PI Steven Kraft, Jeff Beaulieu (Agr. Economics) John Nicklow (Civil Engineering) Michelle Zhu (Computer Science) Raja Sengupta (Geography) McGill George Malanson (Geography) Iowa

  2. The Modeling Approach

  3. Corn Other crops Pasture Wheat Soybean Forest Urban Water Land Use is the Lynchpin Between Social Factors and Environmental Results

  4. 1999 Landuse Polygon Classification

  5. 2000 Landuse Polygon Classification

  6. 2001 Landuse Polygon Classification

  7. 2002 Landuse Polygon Classification

  8. 2003 Landuse Polygon Classification

  9. 2004 Landuse Polygon Classification

  10. Contingent probabilities of land use in a field given land uses in the previous year Probability that a hectare will contain Use last Year Corn Soy Double Grass Forest Total All Corn .07 .81 .01 .11 .00 1.00 All Soybeans .48 .34 .01 .17 .00 1.00 Double Crop.03 .24 .02 .72 .00 1.00 Pasture/Hay.02 .07 .01 .89 .00 1.00 Forest .00 .00 .00 .00 1.00 1.00

  11. Land Use Change Hypotheses • The use chosen for a field is influenced by the physical geographic characteristics of the field such as soil type and slope. (Yes) • (2) The use chosen for a field is influenced by economic costs and opportunities for various uses. (Yes) • (3) The use chosen for a field is influenced by the historical use of that field. (Yes) • (4) The use chosen for a field is influenced by the use of neighboring fields. (No)

  12. Ecological-Economic Production Possibility Frontiers and their Evolution

  13. Create sets of Initial Alternatives (Initial Population) Evaluate fitness, Rank the alternatives GA Logic Choose mates(pairing) Repeat Create offspring (crossover) Multiple Generations lead to Optimal Solution Mutate Optimal Solution

  14. Corn vs. Soybeans: A Classic Trade-off

  15. Corn vs. Hay: A Classic Trade-off

  16. Hay vs. Soybeans: A Classic Trade-off

  17. Sediment and Water Quality Index: Highly Correlated R = -0.97

  18. Phosphorus and Water Quality Index: Highly Correlated R = -0.99

  19. Nitrogen and Water Quality Index: Highly Correlated R = -0.98

  20. Carbon and Water Quality: Complementary R = 0.83

  21. Water Quality and Flood Control: Complementary R=0.70

  22. Carbon and Flood Control: Slightly Complementary R=0.24

  23. Corn and Water Quality: A Trade-Off R = -0.83

  24. Corn and Flood Control: A Slight Trade-off R=-0.24

  25. Carbon and Corn: A Trade-Off

  26. Soybeans and Water Quality: A Trade-Off R=-0.46

  27. Flood Control and Soybean Yield: A Trade-off R = -0.89

  28. Carbon and Soybeans: No Relationship

  29. Hay and Water Quality: Complementary R = 0.90

  30. Hay and Flood Control: Complementary R = 0.71

  31. Carbon and Hay: Complementary

  32. The Overall Ecological-Economic PPF R = -0.83

  33. What We’ve Learned About the PPF • Landscapes that yield high or low sediment yields also yield corresponding N and P yields with correlations of 0.93 - 0.98. • Competition among crops for land produces as classic PPF • Soybeans, and especially corn, is a trade-off with all ecosystem services, but hay is complementary. • Carbon correlates positively with water quality at 0.84. • 5) The current land use pattern is very sub-optimal, more so with respect to ecosystem services than gross margin.

  34. Users Working with the PPF

  35. Users Working with the PPF

  36. Any Questions? Virtual Watershed Diagram from Proposal

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